Energy Efficiency Optimization And Transmission Security For SWIPT Systems

Simultaneous wireless information and power transfer(SWIPT)can transmit both energy and information at the same time,which can improve spectrum efficiency and energy utilization simultaneously,has received growing attentions from both academia and industry.SWIPT enables mobile devices with limited battery capacity to supply information decoding and energy harvesting simultaneously,which can significantly enhance performance of mobile device.With wireless sensors and transceivers getting ever smaller and more energy-efficient,it is expected that SWIPT technique will be widely applied in the design of distributed mobile terminals.Due to the openness of wireless channel,wireless energy carrying communication has variable and controllable coverage,which can correspondingly change the channel conditions between sender,receiver and eavesdropper,making energy efficiency optimization based on physical layer security a problem worthy of in-depth study.With the development of SWIPT technique,the research on energy efficiency optimization and physical layer security problem has important practice and application value.Based on the state of art research results,this dissertation mainly focuses on energy efficiency optimization and physical layer security problem in SWIPT system.The main work and contributions are summarized as follows:1.The energy efficiency optimization for multiple-input multiple-output(MIMO)secure communication SWIPT system with multiple multi-antenna eavesdroppers.In the system the legitimate receiver exploits a power splitting(PS)scheme for decoding information and harvesting energy simultaneously.In order to cripple the eavesdroppers' reception and provide energy for the legitimate receiver to harvest,the transmitter also emits artificial noise(AN)in the transmission.Our interest lies in the joint design of the transmit covariance matrices and PS ratio,such that the secrecy energy efficiency(SEE)is maximized.The SEE maximization problem is a nonconvex fractional problem.To solve this problem,we first convert the primal problem into a parametric program problem by transforming its objective function into a parameterized polynomial subtractive form using fractional programming,and then use the S-Procedure as tool to deal with the non-convexity caused by the CSI errors,convert the semi-infinite constraints into linear matrix inequalities(LMIs),and last transfer the parametric program problem into a optimization problem by means of Taylor series approximation.Furthermore,a two level optimization iterative algorithm is proposed based on Dinkelbach method.2.For the scenario and optimization target mentioned above,two inner level optimization algorithms are proposed.The inner level problem is still difficult to cope with due to coupling between the variables of the objective function and the channel uncertainty.For this non-convex problem,two solutions are proposed.a.Although the approximate equivalent problem is still non-convex,through revealing its hidden primal decomposability,an iterative algorithm based on the Alternating Optimization technique is proposed to optimize all variables in the inner level problem.b.A convex optimization algorithm for variable decoupling based on equivalent transformation.We first deal with the non-convexity caused by CSI errors with the aid of the S-Procedure,and then propose a novel retransformation to remove the coupling between variables.At last we employ the Taylor series approximation techniques to transfer the non-convex problem into a convex optimization problem,and solve this problem by interior point method.3.Energy Efficiency Optimization for Secure MISO SWIPT Systems.In this system the legitimate user employs a power splitting scheme for information decoding and energy harvesting(EH)simultaneously.The information sent to the desired receiver may be wiretapped by the eavesdroppers.An artificial noise is embedded to the information bearing signal to interfere the eavesdroppers and to harvest power by the legitimate user.We consider the secrecy Energy Efficiency maximization problem subject to the transmit power and the EH constraints.Owing to the fractional form and the coupled structure of the objective function,it is difficult to find an optimal solution.In order to obtain the optimal solution,firstly,auxiliary variables are introduced to transform the optimization problem into a two-level optimization problem.The outer level problem is a single-variable optimization problem,which is solved by using a 1-D line search algorithm.For the inner optimization problem,based on SDR technology,the coupling between variables is eliminated by ingenious transformation.We develop a method of combining the penalty function and the difference of two-convex functions(D.C.)approach,and obtain an approximate convex problem by linearizing nonconvex objective functions and constraints.Based on this,an optimal beamforming scheme is finally proposed to obtain the optimal solution.4.Energy Efficiency Optimization for multiuser wireless powered full-duplex systems With self-energy recycling.In the system an energy-constrained full-duplex information transmitter(IT),powered by wireless energy from a dedicated energy transmitter(ET),intends to send confidential information to the information receivers(IR)in the presence of multiple potential eavesdroppers.In the practical scenario of imperfect channel state information and assuming that the idle users need to harvest energy from the ET,we aim to maximize the worst-case secrecy energy efficiency by jointly optimizing the transmit covariance matrix at the ET as well as the information beamforming and artificial noise covariance at the IT and PS at idle users,subject to their individual transmit power constraints and the minimum required power transferred to the idle users.The formulated SEE optimization problem is more complicated than the traditional EE optimization problem due to the non-convex proportional secrecy rate constraints and non-convex SEE function.In order to develop tractable algorithms,we first simplify the SEE optimization problem via exploiting the problem structure.Based on the introduced parameter,we replace the equality constraint with a convex constraint and a concave constraint,and employ the semidefinite relaxation(SDR)and the Taylor series approximation techniques and extended S-procedure approaches to transform the original nonconvex optimization problem into convex problem,which can be efficiently solved.